Review quality and follow-up data in mPower Clinical Analytics
Beginner
Business User
Industry Solutions
Microsoft Cloud for Healthcare
Learn how to use mPower Clinical Analytics to review quality and follow-up data, monitor performance, and support data-driven quality improvement activities.
Learning objectives
By the end of this module, you'll be able to:
- Review Follow-up Recommendations dashboards to monitor recommendation activity and analyze trends across providers, facilities, and reporting periods.
- Evaluate Critical Results dashboards to assess critical findings and communication performance.
- Use Quality Measure dashboards to analyze quality metrics, identify trends, and investigate outliers.
- Explain how Natural Language Understanding (NLU) supports quality identification and review prioritization.
- Review AI Validation dashboards to monitor validation outcomes and evaluate AI performance trends.
- Use dashboard insights to support quality investigations and quality improvement initiatives.
- Evaluate mismatch activity, investigate trends, and support quality improvement initiatives.
Prerequisites
Before starting this module, learners should:
- Basic knowledge of radiology quality review and performance monitoring processes.
- Familiarity with mPower QC and its role in quality management workflows.
- An understanding of quality measures, critical results, and follow-up recommendation concepts.
- Experience navigating dashboards, filters, and visualizations within mPower applications.
- A general understanding of artificial intelligence (AI) concepts used in healthcare quality review workflows.